Abstract: We describe a collection of acoustic and language modeling techniques that
lowered the word error rate of our English conversational telephone LVCSR
system to a record 6.6% on the Switchboard subset of the Hub5 2000 evaluation
testset. On the acoustic side, we use a score fusion of three strong models:
recurrent nets with maxout activations, very deep convolutional nets with 3x3
kernels, and bidirectional long short-term memory nets which operate on FMLLR
and i-vector features. On the language modeling side, we use an updated model
"M" and hierarchical neural network LMs.